Application of Support Vector Machine in the Detection of Fraudulent Financial Statements

摘要：

Auditing practices nowadays have to cope with an increasing number of fraudulent financial statements. Data mining techniques can facilitate auditors in accomplishing the task of detection of fraudulent financial statements (FFS). Considering the character of FFS, this paper designs a FFS detection model based on support vector machine. To perform the experiment, we choose 44 FFS according to the auditing reports and 44 non-fraudulent financial statements(non-FFS) according to some specific standards from listed companies in China during 1999-2002 as training data set. Similarly, 73 FFS and 99 non-FFS during 2003-2006 are chosen as testing data set. We train the model using training data set and apply the trained model to the testing data set, good experimental results are obtained.

关键词： fraudulent financial statement support vector machine detection model

作者: Qingshan Deng

作者单位: School of Software Jiangxi University of Finance and Economics Nanchang, China

会议类型: 国际会议

会议名称: 第四届国际计算机新科技与教育学术会议(2009 4th International Conference on Computer Science & Education)

会议地点: 南京

会议语种:英文

页码: 1056-1059

在线出版日期: 2009-07-25（万方平台首次上网日期，不代表论文的发表时间）